Make a Plot

Load Libraries

Tidy Data

Mappings link data to things you see

gapminder

## # A tibble: 1,704 x 6
##    country     continent  year lifeExp      pop gdpPercap
##    <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
##  1 Afghanistan Asia       1952    28.8  8425333      779.
##  2 Afghanistan Asia       1957    30.3  9240934      821.
##  3 Afghanistan Asia       1962    32.0 10267083      853.
##  4 Afghanistan Asia       1967    34.0 11537966      836.
##  5 Afghanistan Asia       1972    36.1 13079460      740.
##  6 Afghanistan Asia       1977    38.4 14880372      786.
##  7 Afghanistan Asia       1982    39.9 12881816      978.
##  8 Afghanistan Asia       1987    40.8 13867957      852.
##  9 Afghanistan Asia       1992    41.7 16317921      649.
## 10 Afghanistan Asia       1997    41.8 22227415      635.
## # … with 1,694 more rows

p <- ggplot(data = gapminder)

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap,
                          y = lifeExp))

p

This empty plot has no geoms.

p + geom_point() 

A scatterplot of Life Expectancy vs GDP

Build your plots layer by layer

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap,
                          y=lifeExp))
p + geom_smooth()

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Life Expectancy vs GDP, using a smoother.

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap,
                          y=lifeExp))
p + geom_point() + geom_smooth() 

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Life Expectancy vs GDP, showing both points and a GAM smoother.

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap,
                          y=lifeExp))
p + geom_point() + geom_smooth(method = "lm") 

## `geom_smooth()` using formula 'y ~ x'

Life Expectancy vs GDP, points and an ill-advised linear fit.

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap,
                          y=lifeExp))
p + geom_point() +
    geom_smooth(method = "gam") +
    scale_x_log10()

## `geom_smooth()` using formula 'y ~ s(x, bs = "cs")'

Life Expectancy vs GDP scatterplot, with a GAM smoother and a log scale on the x-axis.

p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap, y=lifeExp))
p + geom_point() +
    geom_smooth(method = "gam") +
    scale_x_log10(labels = scales::dollar)

## `geom_smooth()` using formula 'y ~ s(x, bs = "cs")'

Life Expectancy vs GDP scatterplot, with a GAM smoother and a log scale on the x-axis, with better labels on the tick marks.

Mapping aesthetics vs setting them

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap,
                          y = lifeExp,
                          color = "purple"))
p + geom_point() +
    geom_smooth(method = "loess") +
    scale_x_log10()

## `geom_smooth()` using formula 'y ~ x'

What has gone wrong here?

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap,
                          y = lifeExp))
p + geom_point(color = "purple") +
    geom_smooth(method = "loess") +
    scale_x_log10()

## `geom_smooth()` using formula 'y ~ x'

Setting the color attribute of the points directly.

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap,
                          y = lifeExp)) 
p + geom_point(alpha = 0.3) +
    geom_smooth(color = "orange", se = FALSE, size = 8, method = "lm") +
    scale_x_log10()

## `geom_smooth()` using formula 'y ~ x'

Setting some other arguments.

p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap, y=lifeExp))
p + geom_point(alpha = 0.3) + geom_smooth(method = "gam") +
    scale_x_log10(labels = scales::dollar) +
    labs(x = "GDP Per Capita", y = "Life Expectancy in Years",
         title = "Economic Growth and Life Expectancy",
         subtitle = "Data points are country-years",
         caption = "Source: Gapminder.")

## `geom_smooth()` using formula 'y ~ s(x, bs = "cs")'

A more polished plot of Life Expectancy vs GDP.

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap,
                          y = lifeExp,
                          color = continent))
p + geom_point() +
    geom_smooth(method = "loess") +
    scale_x_log10()

## `geom_smooth()` using formula 'y ~ x'

Mapping the continent variable to the color aesthetic.

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap,
                          y = lifeExp,
                          color = continent,
                          fill = continent))
p + geom_point() +
    geom_smooth(method = "loess") +
    scale_x_log10()

## `geom_smooth()` using formula 'y ~ x'

Mapping the continent variable to the color aesthetic, and correcting the error bars using the fill aesthetic.

Aesthetics can be mapped per geom

p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp))
p + geom_point(mapping = aes(color = continent)) +
    geom_smooth(method = "loess") +
    scale_x_log10()

## `geom_smooth()` using formula 'y ~ x'

Mapping aesthetics on a per-geom basis. Here color is mapped to continent for the points but not the smoother.

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap,
                          y = lifeExp))
p + geom_point(mapping = aes(color = log(pop))) +
    scale_x_log10()    

Mapping a continuous variable to color.

Save your work

knitr::opts_chunk$set(fig.width=8, fig.height=5) 

ggsave(filename = "figures/my_figure.png")

here() 

## [1] "/Users/zhuhadar/R-workspace/Lily R Data Vis/LilyFork_kjhealy/stathorizons_0820"

p_out <- p + geom_point(mapping = aes(color = log(pop))) +
    scale_x_log10()

ggsave(here("figures", "lifexp_vs_gdp_gradient.pdf"), plot = p_out)

## Saving 7 x 5 in image

ggsave(here("figures", "lifexp_vs_gdp_gradient.png"), plot = p_out)

## Saving 7 x 5 in image